DS2SIM
Discrete Event Simulation
Complete Exercise Set Tutorials 15
About this document. This set collects all exercises from Tutorials 15
of the DS2SIM course in one place. The exercises cover: basic concepts
and input modelling (Tutorial 1), queueing theory and random number gen-
eration (Tutorial 2), output analysis and con
dence intervals (Tutorial 3),
design of experiments and comparing systems (Tutorial 4), and conceptual
modelling in Enterprise Dynamics (Tutorial 5). Fully worked solutions are
provided in the companion document DS2SIM Worked Solutions.
,DS2SIM: Discrete Event Simulation Exercise Set
Contents
1 Tutorial 1 Simulation Concepts and Input Modelling 3
2 Tutorial 2 Queueing Theory and Random Numbers 5
3 Tutorial 3 Output Analysis and Con
dence Intervals 7
4 Tutorial 4 Experiments and Comparing Systems 10
5 Tutorial 5 Conceptual Modelling in Enterprise Dynamics 14
2
, DS2SIM: Discrete Event Simulation Exercise Set
1 Tutorial 1 Simulation Concepts and Input Modelling
Exercise 1.1
What is simulation?
Exercise 1.2
What is discrete event simulation (DES)?
Exercise 1.3
How does DES relate to continuous simulation and Monte Carlo simulation?
Exercise 1.4
Which steps are to be followed (iteratively) when doing a simulation study, according to
Kettenis and Van der Vorst (chapter 13 of Claassen et al.)?
Exercise 1.5
Why should a simulation model be kept as simple as possible?
Exercise 1.6
What elements should a conceptual model contain?
Exercise 1.7
What is an in
uence diagram?
Exercise 1.8
What is a
ow chart?
Exercise 1.9
What is the reason to include an in
uence diagram in a conceptual model?
Exercise 1.10
What is the dierence between sojourn time and waiting time?
Exercise 1.11
Is it worth considering a (Negative) Exponential distribution for
tting to real data, when the
Gamma distribution does not give a good
t to that data?
Exercise 1.12
Which distributions would you consider if the process shows clear bounds on its duration:
service takes at least 2 minutes and at most 10 minutes?
Exercise 1.13
Suppose you need to simulate the time until a complex product or machine fails, when the
failure can have many causes. What distribution would you consider for modelling the time
until a failure happens?
3
Discrete Event Simulation
Complete Exercise Set Tutorials 15
About this document. This set collects all exercises from Tutorials 15
of the DS2SIM course in one place. The exercises cover: basic concepts
and input modelling (Tutorial 1), queueing theory and random number gen-
eration (Tutorial 2), output analysis and con
dence intervals (Tutorial 3),
design of experiments and comparing systems (Tutorial 4), and conceptual
modelling in Enterprise Dynamics (Tutorial 5). Fully worked solutions are
provided in the companion document DS2SIM Worked Solutions.
,DS2SIM: Discrete Event Simulation Exercise Set
Contents
1 Tutorial 1 Simulation Concepts and Input Modelling 3
2 Tutorial 2 Queueing Theory and Random Numbers 5
3 Tutorial 3 Output Analysis and Con
dence Intervals 7
4 Tutorial 4 Experiments and Comparing Systems 10
5 Tutorial 5 Conceptual Modelling in Enterprise Dynamics 14
2
, DS2SIM: Discrete Event Simulation Exercise Set
1 Tutorial 1 Simulation Concepts and Input Modelling
Exercise 1.1
What is simulation?
Exercise 1.2
What is discrete event simulation (DES)?
Exercise 1.3
How does DES relate to continuous simulation and Monte Carlo simulation?
Exercise 1.4
Which steps are to be followed (iteratively) when doing a simulation study, according to
Kettenis and Van der Vorst (chapter 13 of Claassen et al.)?
Exercise 1.5
Why should a simulation model be kept as simple as possible?
Exercise 1.6
What elements should a conceptual model contain?
Exercise 1.7
What is an in
uence diagram?
Exercise 1.8
What is a
ow chart?
Exercise 1.9
What is the reason to include an in
uence diagram in a conceptual model?
Exercise 1.10
What is the dierence between sojourn time and waiting time?
Exercise 1.11
Is it worth considering a (Negative) Exponential distribution for
tting to real data, when the
Gamma distribution does not give a good
t to that data?
Exercise 1.12
Which distributions would you consider if the process shows clear bounds on its duration:
service takes at least 2 minutes and at most 10 minutes?
Exercise 1.13
Suppose you need to simulate the time until a complex product or machine fails, when the
failure can have many causes. What distribution would you consider for modelling the time
until a failure happens?
3
